Projects

Ongoing Projects

To reduce reliance on other countries for minerals (e.g., coal, rare-earth metals), the USA has seen an invigoration of mining activity in recent years. Unfortunately, miners often have to work in dangerous environments where there is risk of mine explosions, fires, poisonous gases, and flooding in tunnels. The principal objective of this proposal is to devise, design, prototype, and test a fundamentally novel wireless cyber-physical framework of low-cost, energy-efficient, and reliable sensor nodes and commodity smartphones for monitoring, tracking, and communication, to improve miner safety in underground mines.

Identifying communities is an important research topic in traditional as well as online social networks. A community is a densely connected group of nodes/users such that connections between communities are sparse. This project researches community detection in proximity-based mobile social networks based on their spatio-temporal contact profile, recognition of groups with similar activities such as queuing, differentiation of co-located proximity groups.

Completed Projects

Numerous interesting applications have been enabled by embedded sensing technologies and significant research progress on wireless sensor networks. To further ensure a wider adoption of this emerging technology, seamless integration of wireless sensor networks with other existing networks such as WiFi and the Internet is a must. In order to address challenges that arise from such an integrated infrastructure, this project builds HeteroNet, a heterogeneous networking infrastructure, by augmenting an existing flat and homogeneous sensor network test bed. HeteroNet integrates resource constrained sensor nodes and more powerful sensing devices, stationary nodes, mobile nodes, and resource sufficient servers. These nodes communicate in wireless or wired fashion. This test bed establishes an experimental infrastructure to serve as a platform for development, testing, validation, and evaluation of our current research on middleware services for emerging applications on hybrid networks.

The ultimate goal of this dissertation is to develop the required network and systems-level software to support distributed monitoring and control using the resource constrained platforms used in wireless sensor networks. We use a building energy monitoring and control application to demonstrate the usefulness of our system. Building energy management is necessary to tailor building performance to the occupant rather than forcing the occupant to change their behavior in order to conserver energy. Wireless sensor networks are a good fit to solve this problem because they can be used to create low-cost communication networks. These networks can be used to share information between separate systems. However, current wireless sensor network architectures and systems have focused on centralized systems with less emphasis on peer to peer information sharing.

Our main contributions are in two distinct areas. 1) WSN networking and systems. We are developing a complete WSN architecture that is better optimized for the problem of distributed monitoring and control using peer to peer communication. Central to our approach is a novel multicast implementation for IPv6 WSNs that we have developed. Using multicast communication allows sensor nodes to efficiently share data in a distributed fashion while the use of standard IPv6 communication greatly improves interoperability. 2) Building energy management. Previous studies of building energy consumption have focused on large appliances and group all small devices into the category of miscellaneous electrical loads. By embracing WSN-based monitoring and developing improved non-intrusive load monitoring techniques for these devices we are able to provide a more detailed analysis of building energy consumption and how human behaviors affect consumption. Our preliminary studies have demonstrated a 7%-14% reduction in energy consumption using a distributed WSN-based control system.

In the future, we are likely to see a tremendous need for context-aware applications which adapt to available context information such as physical surroundings, network or system conditions. The success of end-to-end adaptation relies on accurate and timely knowledge of the changing context information. This project aims to provide a fundamental support for context-aware applications - a context information collection service. This service delivers the right context information to the right user at the right time. The complexity of providing the context information service arises from (i) dynamically changing status of information sources; (ii) diverse user requirements in terms of Quality of Service (QoS: such as response timeliness or reliability etc.) and Quality of Data (QoD, such as data accuracy or freshness); and (iii) constantly changing system conditions.

In this project, we take into consideration the dynamic factors mentioned above and focus on addressing the tradeoffs between QoS, QoD and resource consumption by exploiting the tolerance of applications to quality violations. The objective is to ensure that applications receive the information at the desired levels of quality while ensuring effective utilization of underlying resources. We have focused on designing adaptive and cost-effective algorithms for the representation, collection and maintenance of the enormous amount of dynamic context information in heterogeneous distributed systems. These algorithms are tailored for multimedia services, mobile applications and real-time applications. In addition to these algorithmic efforts, we have designed a middleware framework supporting context awareness.

The main objective of this project is to explore, develop, and demonstrate a robot prototype system for an unmanned process, or process section, to improve safety and increase reliability and to be operated in remote and harsh environments. The presence of toxic gasses (such as H2S) and very high outdoor temperatures in the Middle East region poses great risks to staff on the site of oil and gas facilities, so the clear benefits of using robotic technology for the relevant tasks such as inspection, operations and maintenance of oil and gas facilities are reliability, robustness and flexibility. Such a system will have to perform a number of different tasks, some of which are guided by a remotely located operator or operation team, and some of which are performed automatically by the robot without human intervention.

Emerging applications using wireless sensor networks for critical areas such as environmental monitoring and emergency response highlight the urgent need for more powerful algorithms for tracking amorphous events or phenomena with dynamic identities. Several such events may combine into a large whole or one event may disintegrate into several smaller ones. Current efforts in event detection and tracking have mostly assumed that either events remain distinct, never crossing or passing too close together to become indistinguishable, or if they do cross that they were identified prior and nothing new has formed. This project addresses the research challenges in designing and implementing a system that is capable of tracking events with or without well-defined shapes and identities in the presence of stringent energy constraints and unpredictable network failures posed by wireless sensor networks. Specific research objectives include: design and evaluation of algorithms that detect and track any types of events including amorphous phenomena with dynamic signatures and events that possess a static shape with a crisp boundary; design and evaluation of algorithms that form and reform communication structures around events of interest; and development of an integrated system that provides interfaces to high level application tasks to execute on each identified event. Successful completion of this project will result in a rich set of tools that can be used by applications monitoring all different types of events.

Sensor devices are promising to revolutionize our interaction with the physical world by allowing continuous monitoring and reaction to natural and artificial processes at an unprecedented level of spatial and temporal resolution. As sensors become smaller, cheaper and more configurable, systems incorporating large numbers of them become feasible. Besides the technological aspects of sensor design, a critical factor enabling future sensor-driven applications will be the availability of an integrated infrastructure taking care of the onus of data management. Ideally, accessing sensor data should be no difficult or inconvenient than using simple SQL.

In this project, we investigate some of the issues that such an infrastructure must address. Unlike conventional distributed database systems, a sensor data architecture must handle extremely high data generation rates from a large number of small autonomous components. And, unlike the emerging paradigm of data streams, it is infeasible to think that all this data can be streamed into the query processing site, due to severe bandwidth and energy constraints of battery-operated wireless sensors. Thus, sensing data architectures must become quality-aware, regulating the quality of data at all levels of the distributed system, and supporting user applications' quality requirements in the most efficient manner possible. The long term goal of this project is to integrate various ideas at the sensor, middleware and application levels into a unified system which will be easily customized to individual sensing applications, but will be generic and modular enough to be useful to a large class of such applications.

Release of chemicals or biological agents in the subsurface often results in plumes migrating in the medium, posing risk to human and ecological environments. Temporal and spatial monitoring of the plume concentrations are needed to assess risk, make decisions and take remedial action. Current underground contaminant plume monitoring technologies are inefficient, expensive and ineffective. Wireless sensor technologies have the potential to dramatically improve this process.

A closed-loop system integrating wireless sensor network based monitoring with numerical models for plume tracking is being developed, in which sensor data continuously calibrates and validates the system identification and prediction models, while the output from these models direct the sensor network operation to optimize constraints such as accuracy and power consumption. The system is based on a novel virtual sensor network architecture with broader applicability beyond plume tracking. Algorithms and protocols being developed support the formation, usage, adaptation and maintenance of dynamic subsets of collaborating sensors, named Virtual Sensor Networks (VSNs). VSN protocols for collaboration among groups of sensors will greatly ease the task of deploying sensor networks, especially in environments where multiple geographically overlapping applications are deployed. A proof-of-concept laboratory test bed that captures the complex subsurface processes is used for integration and evaluation of VSN protocols. This interdisciplinary project significantly advances the state-of-the art in subsurface plume tracking and sensor networking technologies. It stimulates a unique partnership of electrical engineers, computer scientists and environmental researchers, and demonstrate closed-loop operation of computer models and sensor networks to solve complex environmental problems.